Adjustment for Covariate Measurement Errors in Complex Surveys: A Simulation study of Three Competing Methods
Abstract
In sample surveys, the uncertainty of parameter estimates comes from two main sources: sampling and measuring the study units. Some aspects of survey errors are quite well understood (e.g. sampling errors, nonresponse errors) and reported but others, like measurement errors, are often neglected. This thesis studies measurement uncertainty in covariates.
Focus is on the adjustment for covariate measurement errors in logistic regression for cluster-correlated data. Three methods for adjustment for covariate measurement errors in surveys are studied. The methods are Maximum Likelihood, Multiple Imputation and Regression Calibration. These methods require information obtained from validation study.
The thesis consists of a theoretical part and extensive Monte Carlo simulation experiments. At the first simulation experiment, the simulation study is conducted with artificial data and with independent observations to test and have experience of the three methods: MI, ML and RC. The second and third simulation study is performed with cluster-correlated data. In these simulation studies, the first simulation uses artificial data and the latter uses real data. In both simulations regression calibration and multiple imputation approaches are examined in various simulation designs.
The quality of the methods is assessed by the bias and accuracy. The bias is measured by absolute relative bias percentages (ARB%) and the accuracy by relative root mean-squared error percentages (RRMSE%).
The results suggest that additional information from validation (calibration) data enables more accurate estimates in terms of bias percentages.
Author
Maria Valaste
Additional Information
- Peer-Reviewed: no.
- Open Access: yes.
- Cite as: Valaste, M. (2015). Adjustment for Covariate Measurement Errors in Complex Surveys: A Simulation study of Three Competing Methods [doctoral dissertation, University of Helsinki]. Helda. http://urn.fi/URN:ISBN:978-951-51-0846-3
